Impact of Privatization and Deregulation on the Use of Telephone Services in French-Speaking Countries (1988-1998)

Jocelyn NADEAU <jocelyn@cidif.org>
Centre International pour le Développement de l'Inforoute en Français
Canada

André LECLERC <andre@cuslm.ca>
University of Moncton
Canada

Mario FORTIN <mfortin@courrier.usherb.ca>
Université de Sherbrooke
Canada

Contents

Introduction

In response to technological advance, the rise of multinational corporations, the restrictions imposed by regulation, and increasing competition on domestic markets, the structure of the telecommunications industry is being reshaped around the globe. How well national economies are able to adapt to these changes will determine how much benefit they will derive from the business opportunities presented by new forms of commerce. This is even more critical for the developing countries, since there is real risk that existing income gaps will widen further.

These trends have prompted reform in three areas affecting the structure of the telecommunications industry: regulation, ownership, and competition. The French-speaking nations collectively known as "la Francophonie" have certainly felt the winds of change. In France, for example, an independent regulatory agency was first formed, and cross-ownership involving French partners was allowed. Later the industry was opened to competition. In Canada, too, the industry was thrown open to competition first in long-distance service and later in local service. Privatization and greater competition, therefore, can be seen as two thrusts of a single strategy. In theoretical and empirical terms, however, there is considerable debate about the effects of this strategy. Attempts to measure the effects of privatization have had mixed results. Political control does not necessarily turn out to be more inefficient than market forces. Meanwhile, it was expected that increased competition would lead to lower prices and increased consumption of services with an expansion of supply. In the telecommunications sector, however, the increase in competition has been seen more in specialized services (long-distance calling, Internet services) than in basic residential service. Overall, prices for specialized services have fallen as new competitors have flocked to the industry. The impact on the price of universal services is not so clear. The debate has become essentially empirical in nature, and that is why an in-depth examination of telecommunication enterprise in the countries that comprise la Francophonie seems timely.

Regulatory change

One thing is clear. By the end of the period covered by this study (1988-1998), the telecommunications industry had become more highly structured, with many countries creating independent regulatory agencies. It was also more open to cross-ownership, the dominant form being enterprise co-owned by the local government and foreign corporations.

What was the cause of these changes? Regulatory change was generally guided by the six main principles of competition developed as part of the General Agreement on Trade in Services, signed in 1994 during the final round of the World Trade Organization. These six principles are as follows: competition safeguards to guarantee that major suppliers do not engage in anti-competitive practices (including a ban on inter-service subsidies and an agreement to disclose technical and business information to competitors); interconnection (to ensure interconnection between new suppliers and the major supplier with non-discriminatory rates and the release of information on procedures, agreements and interconnection rates); transparent, non-discriminatory and impartial administration of the universal service obligation; access to information on licensing criteria and on reasons for the denial of a license; independence of regulatory agencies via-à-vis all providers of basic telecommunications services; and the establishment of transparent, objective, timely and non-discriminatory procedures governing the allocation and use of scarce resources (ITU, 1997b, 101-105). With these principles in mind, we will now revisit the structural changes that have taken place in the telecommunications industry, focusing on certain key aspects.

Figure 1 shows changes in the number of independent regulatory agencies in the countries of la Francophonie.(1) The trend changes sharply as of 1995. From 1988 to 1994, the number of independent agencies increased from two to five. At the start of the period, Canada and St. Lucia were the only two to have independent agencies. From 1995 on, however, the upward trend increases sharply, reaching 17 in 1998 (one-third of the countries). The Treaty on Communications appears to have had a strong impact on countries of la Francophonie.


Figure 1

Ownership structure

The other major change in industry structure occurs in the ownership structure of the primary provider of basic telephone service. This is shown in Figure 2. There are four basic options: state controlled, public corporation, partial privatization and total privatization. This last option appears rarely in the countries of la Francophonie; it was adopted by only two countries, Canada and St. Lucia, in 1987 and 1999 respectively. Under the other three options, change takes place more slowly. First there is the transition from production by the state's public service to production by a public corporation. Later, this corporation becomes associated with partners, often in the form of a strategic alliance with a foreign corporation. This corporation provides the capital and knowledge needed to modernize and expand the network. From 1987 to 1999, for example, the number of countries where service is provided by the state decreased from 19 to 4. On the other hand, the number of public corporations increased from 19 to 25, and the number of cases of partial privatization from 11 to 20.


Figure 2

It should also be noted that the market structure varies widely with the type of service. As shown in Figure 3, for example, competition is much stronger in specialized services like cellular than in basic services such as local service and domestic and international long distance. Of the 51 countries, 37 have monopolies for local service and 41 for domestic long distance. In specialized services, the number of monopolies falls to 18, while the number of competitive environments increases to 14 (analog cellular) and 19 (digital cellular).(2)


Figure 3

Impact of the transformations

The purpose of this study is to analyze the impact of the transformations that have taken place in the industry's regulatory and ownership structure on the price and quantitative use of basic telephone service in the countries of la Francophonie. Drawing on data from the ITU and the World Bank, we developed a database covering 48 of the 52 states belonging to la Francophonie.(3) As shown in Table 1, by far most of the countries are among the developing nations. If we use the classification proposed by the World Bank, 23 countries are the category of low-income countries, 13 are low-middle-income countries, 6 are high-middle-income countries, and the 6 remaining are high-income countries.(4)

Table 1: Countries of la Francophonie by level of development
Low-Income Countries
Benin
Burkina Faso
Burundi
Cambodia
Cameroon
Central African Republic
Chad
Comoros
Congo
Guinea
Guinea-Bissau
Haiti
Ivory Coast
Laos
Madagascar
Mali
Mauritania
Moldavia
Niger
São Tomé and Príncipe
Senegal
Togo
Vietnam
Low-Middle-Income Countries
Albania
Bulgaria
Cape Verde
Djibouti
Dominican Republic
Egypt
Equatorial Guinea
Lithuania
Macedonia
Morocco
Romania
Tunisia
Vanuatu
High-Middle-Income Countries
Czech Republic
Gabon
Lebanon
Mauritius
Poland Saint Lucia
High-Income Countries
Belgium
Canada
France
Luxembourg
Slovenia Switzerland

The price and consumption of telephone services are closely tied to a country's level of development. Figures 4 and 5 show for the period 1988-1998, respectively, changes in the number of trunk telephone lines per 100 residents and monthly rates for basic telephone service in U.S. dollars by the country's income class. In the case of telephone lines, there is a wide gap between low-income countries and high-income countries. From 1988 to 1998, the number of trunk lines rose from 42 to 58 per 100 residents in high-income countries, but in low-income countries the same figure rose from 0.8 to 1.4. Thus the gap actually widened over the period.


Figure 4


Figure 5

The situation is different for the low-middle-income and high-middle-income countries. While the number of main lines per 100 residents rose by 38 percent in high-income countries during the period, it jumped by 94 percent (from 6.7 to 13) in low-middle-income countries, and by 165 percent (from 8.2 to 22.7) in high-middle-income countries. In terms of prices, the trends are altogether different. In high-income countries, there was a gradual rise in monthly rates. In the other three groups, prices increased slowly until 1993, then started to decline, particularly in the low-income and high-middle-income countries.

Method and results

We applied the two-step least-squares method (TSLS) to estimate two equations with, as dependent variables, the logarithmic transformation of the monthly basic telephone rate in U.S. dollars and the number of trunk telephone lines in service by household. (The complete model is described in the appendix.) This method was selected because the number of subscribers explains the price, and the price explains the number of subscribers. Consequently, estimation by ordinary least squares would have produced biased estimates of these variables. Moreover, because we have no exogeneous instruments, the two equations would be under-identified, making it impossible to estimate the coefficients of the structural form. In order to obtain an instrument for each of these variables, we excluded 1988 from the database and used, in addition to all the exogeneous variables, the one-year lag of price to serve as an instrument in the number of subscribers equation, and the one-year lag of the number of subscribers to serve as an instrument in the price equation. Thus, each equation is properly identified. At the same time, we excluded the binary variable for 1989 so that the coefficients of annual dummies measure the differences relative to 1989. Since the countries of are varying sizes, we used the weighted least-squares method, weighting each country according to the logarithm of the number of households. This eliminated a source of heteroscedasticity. Given that there remained some unexplained heteroscedasticity, we used White's method to ensure that the variance matrix was correctly estimated. The results of the estimation are shown in Table 2 (price equation) and Table 3 (number of subscribers equation).

Price equation

Number of subscribers equation

Total effect of GDP on price and number of lines

Conclusion

Privatization causes the price of basic telephone service to rise and the number of subscribers to grow. Recent changes in regulation and in the nature of service providers will thus have more impact on price than on consumption. The ownership and regulation variables represent an important aspect of the debate on the development of the telecommunications industry. Our results also suggest that these are not the only variables influencing price and quantity.

This study looks only at the countries of la Francophonie. It must be realized that these countries have not been in the vanguard of the worldwide restructuring of the telecommunications industry. As we have shown, in fact, they have tended simply to react to trends imposed by other countries. It would be interesting to expand the scope of our study to all countries of the world to obtain much wider institutional diversity and to examine a larger number of countries where structural changes have recently taken place. This would also allow us to derive results from a much richer database.

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Appendix: estimated model

Two equations were estimated. In the first, the dependent variable is the logarithm of the monthly rate for basic telephone service in U.S. dollars (LN ABON $US). This variable is related to four types of explanatory variables. The first, the logarithm of gross domestic product (GDP) per household (LN PIB EN $US / MÉNAGES), accounts for the impact of households' ability to pay on the price of service. This variable should have a positive effect on the monthly rate. We next integrate three series of binary variables. The first consists of a variable for each country to capture the potential fixed effects by country attributable, for example, to differences in production costs, in producers' profit margins or in the indices used to establish the real price of products.(5) In order to avoid perfect multicolinearity with the constant, we arbitrarily chose to eliminate Mauritius from the binary variables. The coefficients of the binary variables per country thus measure the price difference relative to Mauritius.

Second, because the database is constructed as a panel, one binary variable is added for each of the years 1989-1998 in order to capture the potential fixed effects per year, particularly those due to factors such as technological development. The coefficients of the binary variables per year thus measure the change in the constant relative to 1988. The third series of binary variables integrates information on regulatory changes. We saw earlier that ITU data allow us to determine for each country the year in which an independent regulatory agency was created. The variable AGENCE allows the impact of this decision on rates to be tested. The same database can be used to follow changes in the ownership model of the principal supplier of basic telephone service. We used partial or total private ownership as the point of comparison, and we included two binary variables to test the price effect of the production of service by government (STATUT PUBLIC) or by public corporation (STATUT SOCIÉTÉ D'ÉTAT) on monthly subscriptions. Last, we include the logarithm of the relationship between the number of trunk lines and the number of households (LN LIGNES PRINC./ MÉNAGES) in the price equation. The partial effect of this variable on price is not clear. First, the larger the subscriber base, the more valuable is telephone service to individual subscribers, since each has access to a larger pool of subscribers. It can then be expected that the company will command a higher price for telephone service. Second, when a larger percentage of households are subscribers, the company is able to amortize fixed costs against a larger subscriber base and thereby reduce the unit cost of service. This second phenomenon may therefore exert a negative influence for this variable on the price of service.

The first equation estimated was the following:

The second equation aimed to estimate the determinants of the number of subscribers relative to the number of households. Compared with the first equation, the second simply switched the position of the two variables, the logarithm of subscription rates and the number of trunk lines in operation. The interpretation of the partial effect of the variables is direct. The logarithm of real GDP per household measures the income elasticity of demand of telephone service. The binary variables by country and by year measure the differences in the number of lines per household relative to the base case. It was hoped that the upward trend in the number of lines would be partially captured by this variable, with some of the effect associated with the upward trend in GDP per household. The binary variables for status and agency measure their effect on the number of households serviced relative to the situation of a private company. Lastly, the price captures the usual negative effect of price on the demand for services. The second equation is thus as follows:

Table 2: Estimation of price equation using TSLS
Variables Coefficient1 Std. dev. T-statistic Prob.
C -1.159352** 0.673853 -1.720484 0.0861
LN(PIB EN $US / MÉNAGES) 0.428398* 0.074606 5.742165 0.0000
ALBANIE -2.268851* 0.226805 -10.00353 0.0000
BELGIQUE 1.140118* 0.104270 10.93430 0.0000
BENIN 0.099715 0.190686 0.522927 0.6013
BULGARIE -1.369717* 0.231235 -5.923474 0.0000
BURKINAFASO -0.124561 0.228384 -0.545404 0.5858
BURUNDI -1.046211* 0.255360 -4.097010 0.0001
CAMBODGE 1.035517* 0.304829 3.397046 0.0008
CAMEROUN -0.617157* 0.200473 -3.078503 0.0022
CANADA 0.843454* 0.136307 6.187896 0.0000
CAPVERT 0.040735 0.090217 0.451527 0.6519
CENTRAFRIQUE -0.040475 0.232773 -0.173881 0.8620
COMORES 0.634781* 0.191888 3.308089 0.0010
CONGO 0.993524* 0.162213 6.124813 0.0000
COTEDIVOIRE 0.319813* 0.136190 2.348294 0.0194
DJIBOUTI 1.383678* 0.122719 11.27522 0.0000
DOMINIQUE 0.677327* 0.206381 3.281930 0.0011
EGYPTE -0.876212* 0.137615 -6.367136 0.0000
FRANCE 0.730151* 0.106707 6.842607 0.0000
GABON 1.199102* 0.155436 7.714438 0.0000
GUINEE -1.148128* 0.236529 -4.854062 0.0000
GUINEQUATORIALE 0.278019 0.204997 1.356214 0.1758
GUINEEBISSAU -0.493968* 0.195084 -2.532077 0.0117
HAITI 0.201294 0.186652 1.078447 0.2815
LAOS -0.407030** 0.222210 -1.831738 0.0677
LIBAN 0.017471 0.117513 0.148671 0.8819
LITUANIE -0.738948* 0.148847 -4.964469 0.0000
LUXEMBOURG 0.527797* 0.147603 3.575795 0.0004
MACEDOINE -0.508632* 0.240011 -2.119202 0.0347
MADAGASCAR 0.125404 0.262266 0.478154 0.6328
MALI -0.480906* 0.239626 -2.006899 0.0454
MAROC 0.033450 0.222739 0.150175 0.8807
MAURITANIE 0.415275** 0.229539 1.809170 0.0712
MOLDAVIE -0.527975** 0.309086 -1.708181 0.0884
NIGER -0.009954 0.258813 -0.038461 0.9693
POLOGNE -0.198512 0.328680 -0.603968 0.5462
REPUBLIQUETCHEQU -0.269635* 0.089593 -3.009574 0.0028
ROUMANIE -0.385121* 0.112027 -3.437753 0.0006
SAINTELUCIE 0.922344* 0.088403 10.43339 0.0000
SAOTOMEPRINCIPE -0.490141* 0.203849 -2.404437 0.0167
SENEGAL -0.119877 0.136633 -0.877364 0.3808
SLOVENIE 0.444341* 0.139152 3.193205 0.0015
SUISSE 1.275615* 0.120361 10.59820 0.0000
TCHAD 0.535344** 0.323970 1.652447 0.0992
TOGO -0.193128 0.208957 -0.924250 0.3559
TUNISIE -0.276888* 0.058870 -4.703334 0.0000
VANUATU 0.504494* 0.110881 4.549857 0.0000
VIETNAM 0.852856* 0.242497 3.516976 0.0005
AN3 0.004899 0.106701 0.045912 0.9634
AN4 0.087976 0.083720 1.050833 0.2940
AN5 0.046839 0.088879 0.526996 0.5985
AN6 0.144000 0.090355 1.593710 0.1118
AN7 0.098496 0.081894 1.202718 0.2298
AN8 0.142397 0.087267 1.631738 0.1035
AN9 0.149060 0.090515 1.646792 0.1004
AN10 0.261297* 0.093582 2.792163 0.0055
AN11 0.187369** 0.104780 1.788221 0.0745
AGENCE 0.108145** 0.055402 1.951985 0.0516
STATUT PUBLIC -0.307800* 0.140843 -2.185408 0.0294
STATUT SOCIÉTÉ D'ÉTAT -0.183994* 0.072446 -2.539718 0.0115
LN(LIGNES PRINC. / MÉNAGES) -0.424001* 0.062220 -6.814602 0.0000
Weighted statistics:
  • R2: 0.924018
  • Mean of dependent var.: 1.464251
  • Adjusted R2: 0.912224
  • Std. dev. of dependent var.: 1.079785
  • Std. dev. of regression: 0.319907
  • Sum of square of residuals: 40.21990
  • F- statistic: 79.71301
  • Prob. (F-statistic): 0.000000

* coefficient significant at the 5 percent level.
** coefficient significant at the 10 percent level.

Table 3: Estimation of subscription number equation using TSLS
Variables Coefficient1 Std. dev. T-statistic Prob.
C 1.727089* 0.679841 2.540432 0.0115
LN(PIB EN $US / MÉNAGES) 0.206910* 0.073892 2.800166 0.0054
ALBANIE -2.197387* 0.186027 -11.8122 0
BELGIQUE 0.822635* 0.120869 6.805982 0
BENIN -2.436266* 0.18686 -13.0379 0
BULGARIE 0.423413* 0.148992 2.841844 0.0047
BURKINAFASO -3.030964* 0.210866 -14.3739 0
BURUNDI -3.348674* 0.206417 -16.22286 0
CAMBODGE -4.188240* 0.357016 -11.73124 0
CAMEROUN -2.770888* 0.137243 -20.18963 0
CANADA 1.200575* 0.133232 9.011171 0
CAPVERT -0.447259* 0.147704 -3.028072 0.0026
CENTRAFRIQUE -3.041097* 0.223923 -13.58101 0
COMORES -1.939581* 0.202692 -9.569083 0
CONGO -1.983066* 0.20505 -9.671135 0
COTEDIVOIRE -1.656890* 0.139683 -11.86182 0
DJIBOUTI -1.446566* 0.215798 -6.703346 0
DOMINIQUE 0.732825* 0.127339 5.754897 0
EGYPTE -0.872637* 0.118396 -7.370501 0
FRANCE 0.904382* 0.124013 7.292625 0
GABON -0.990368* 0.148419 -6.67278 0
GUINEE -3.557704* 0.167996 -21.17734 0
GUINEQUATORIALE -2.146055* 0.211666 -10.13889 0
GUINEEBISSAU -1.844137* 0.197175 -9.352773 0
HAITI -1.800221* 0.221724 -8.119186 0
LAOS -2.837297* 0.226356 -12.53468 0
LIBAN 0.233343* 0.103888 2.2461 0.0253
LITUANIE 0.500642* 0.150877 3.318208 0.001
LUXEMBOURG 0.906198* 0.138546 6.540758 0
MACEDOINE 0.247921* 0.113523 2.183896 0.0296
MADAGASCAR -2.952945* 0.230823 -12.79314 0
MALI -3.412199* 0.194622 -17.53247 0
MAROC -0.885376* 0.158055 -5.601699 0
MAURITANIE -2.702212* 0.208734 -12.94571 0
MOLDAVIE 0.167357 0.234972 0.712242 0.4767
NIGER -3.529417* 0.236977 -14.8935 0
POLOGNE -0.154101 0.108297 -1.422957 0.1555
REPUBLIQUETCHEQU 0.201686* 0.095595 2.109787 0.0355
ROUMANIE -0.043124 0.110438 -0.390481 0.6964
SAINTELUCIE 0.510634* 0.130205 3.921781 0
SAOTOMEPRINCIPE -1.355160* 0.201384 -6.729224 0
SENEGAL -1.519062* 0.149573 -10.15599 0
SLOVENIE 0.577599* 0.088117 6.554886 0
SUISSE 0.922277* 0.139102 6.630235 0
TCHAD -4.192794* 0.310116 -13.52007 0
TOGO -2.527638* 0.182865 -13.82241 0
TUNISIE -0.552100* 0.086224 -6.403108 0
VANUATU -0.995379* 0.162634 -6.120342 0
VIETNAM -2.389712* 0.392857 -6.082913 0
AN3 0.051249 0.057538 0.89071 0.3736
AN4 0.122328* 0.053797 2.273901 0.0235
AN5 0.172792* 0.051181 3.376082 0.001
AN6 0.245176* 0.048451 5.060327 0
AN7 0.332616* 0.048888 6.803607 0
AN8 0.372817* 0.054132 6.887154 0
AN9 0.477029* 0.056031 8.513684 0
AN10 0.595023* 0.066948 8.887904 0
AN11 0.758456* 0.077398 9.799388 0
AGENCE -0.013683 0.035033 -0.390577 0.6963
STATUT PUBLIC 0.117575 0.076582 1.535268 0.1255
STATUT SOCIÉTÉ D'ÉTAT 0.074628 0.057637 1.294787 0.1962
LN(ABONNEMENT EN $US) -0.167024* 0.072984 -2.288499 0.0226
Weighted statistics:
  • R2: 0.989116
  • Mean of dependent var.: 2.286158
  • Adjusted R2: 0.987431
  • Std. dev. of dependent var.: 1.919874
  • Std. dev. of regression: 0.215237
  • Sum of squares of residuals: 18.25283
  • F-statistic: 586.6911
  • Prob. (F-statistic): 0.000000

* coefficient significant at the 5 percent level.
** coefficient significant at the 10 percent level.

Notes

  1. A complete lack of information obliges us to eliminate here the Republic of the Seychelles, leaving only 51 countries.
  2. It might be argued that these figures are misleading because they represent what legislation allows rather than the true market situation. Even when a company is alone in a liberalized market, however, potential competition has a real effect on its behavior. The legislative context is often a better indicator of the true situation than observation.
  3. One of the major problems we encountered, one common to many studies on the developing countries, was incomplete data. The choices were to eliminate such countries or to attempt to complete the data series. In the case of la Francophonie, the first choice was not an option, because the vast majority of countries fell into this category. We thus completed the series for our two basic indicators by assuming that missing figures were equal to the last figure available. In effect, this strategy assumes that data are only supplied to the ITU when they change. Despite this stratagem, three countries had to be eliminated from the study: Monaco, because no price information was available, and the Democratic Republic of the Congo and Rwanda, because the population data fluctuated too widely.
  4. During the study period, some countries' political status changed, such as the Czech Republic and Macedonia. In these cases, the ITU completed the series using those available before the change in status.
  5. We will see shortly that a significant component of fixed effects by country is associated with the level of economic development. It is not surprising, that biases appear, therefore, since real GDP per resident is not an adequate representation of the real differences in living standards for countries with significant development deficits.